Data scientists spend 80% of their time cleaning data. Here's how to do it efficiently.
The 10 Steps
1. Remove duplicates. 2. Fix encoding issues (UTF-8 everywhere). 3. Standardize dates (pick one format). 4. Trim whitespace. 5. Handle missing values (delete row, fill with mean/median, or flag). 6. Fix data types (numbers stored as text). 7. Standardize categories ('US'/'USA'/'United States' → one value). 8. Remove outliers (or flag them). 9. Validate ranges (age = -5? Nope). 10. Cross-reference (check against source).
Start by viewing your data in our CSV viewer to spot issues visually.